Developing machine learning systems is painfully slow. Machine learning (ML) models take months to develop and deploy. By the time ML models are deployed into production, development velocity is at a crawl. Let's understand why.
Developing and maintaining machine learning (ML) systems is costly. ML practitioners (Machine Learning Researchers, Engineers, Developers, and Data Scientists) incur multiple costs as they shift from prototype to production. Understanding real costs (true costs) of ML systems allows companies to better develop prototypes and deploy models into production.
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